2. Understanding the Digital Readiness Index

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Understanding a customer’s digital readiness is critical to helping them in their digital transformation and to recommending the right Sight Machine products. An integral part of our engagement model is utilizing the Digital Readiness Index (DRI), a methodology that Sight Machine uses to evaluate a manufacturer’s readiness for digital transformation and identify the projects likely to deliver the most impact.

You can use DRI to identify which organizational and technical assets an enterprise customer already has in place, as well as provide the customer with a roadmap for prioritizing plants, building capabilities, and benchmarking against peers. DRI is also a valuable framework that you can use to help manage customer expectations and set achievable goals that will add value for the customer.

With our experience in executing Digital Manufacturing initiatives at numerous plants, we have learned that there are a key set of organizational and technical readiness attributes that impact the success of projects.

This does not mean that your plant needs a minimum level of readiness to get started; instead, it means that understanding your readiness will:

  • Help you select the right short-term projects that are likely to succeed
  • Help you understand the factors that will impact scope/timeline/budget requirements
  • Provide you with guidance on what you need to work on to enable more advanced capabilities

There are two components (or measurements) to our Digital Readiness Index:

  • Organizational Readiness (or Y axis): Does your customer have the necessary institutional support to begin the digital transformation?
  • Technical Readiness (or X axis): Does your customer have the right IT infrastructure in place to be successful?

Understanding the Organizational Readiness Axis

DRI’s Organizational Readiness axis measures whether a customer has the institutional support needed by evaluating factors such as commitment, budget, and staffing. You can use this information to determine the capability for new infrastructure and the likelihood of successful adoption.

Organizational Readiness consists of three categories:

  • Commitment and Budget: For a large project to succeed, the customer requires buy-in from staff at many levels (e.g., machine operator, plant manager, executive management, and IT). In addition, the customer must have sufficient budget available, both for upgrading the underlying infrastructure (e.g., networking) and for the analytics project itself.
  • Skills and Resourcing: We have categorized the main staff members needed for digitization projects. These include, for example, subject matter experts, data scientists, and change management experts who can implement the identified improvements.
  • Change Management: To capture the value opportunities identified by analytics, the customer needs the right leadership in place to implement process, staff, and product changes.

To determine a customer’s readiness from an organizational standpoint, complete the following assessment with the necessary stakeholders.

Organizational Readiness Assessment




Partly True


Commitment and Budget

An onsite champion is committed to the success of this project.

Local staff support the project and will work to achieve success.

Plant leadership are committed to the project’s success.

Digital transformation is a corporate priority.

IT will allocate resources to achieve project success.

Sufficient budget has been allocated to flow data into a system of record (e.g., historian).

Sufficient budget has been allocated for the data analytics project.

Skills and Resourcing

Technical experts in the customer’s manufacturing process are available and willing to help with context and validation.

The customer has access to experts who understand which sensors and machines to connect and what kind of data they want to export from the machines.

The customer has access to IT engineers for connecting sensors to networks and data storage.

The customer has staff to manage how data is stored and accessed.

The customer has data scientists who can interpret and draw insights using analytics tools.

The customer has access to staff who can develop custom data applications needed in the factory.

Change Management

The production process has precisely defined and measurable key performance indicators (KPIs).

The customer has the right leadership in place to implement process, staff, and product changes to capture value identified by analytics.

Considering the responses to the assessment, you can determine how prepared the customer is organizationally, and suggest appropriate next steps. For more information, see Managing Customer Expectations in Understanding the Digital Readiness Index.

Understanding the Technical Readiness Axis

DRI’s Technical Readiness axis measures the customer’s technical landscape by evaluating data, policy, and knowledge infrastructure.

Technical Readiness consists of three categories:

  • Data Connectivity and Accessibility: This measures the availability and breadth of data, a fundamental requirement for any digital transformation project. It is important to find out if the machines have sensors, if they are networked, and whether data is flowing into a system of record or database. To enable more sophisticated analytics, part serial numbers or batch numbers with timestamps should be captured at each process step.
  • Cloud and Security Strategy: It is important to ask if the data is remotely and securely accessible, if the customer has an existing strategy for working with cloud providers, and if there are any special requirements to segregate sensitive data, such as classified, ITAR, or HIPAA data.
  • Data Awareness: The customer should have existing documentation or expertise to interpret the data coming from the machines and understand how it maps to the physical process.

To determine a customer’s technical readiness, complete the following assessment with the necessary stakeholders.

Technical Readiness Assessment




Partly True


Data Connectivity and Accessibility

The key machines are equipped with sensors.

Machines are network-accessible.

The key machines are networked.

Data is flowing into a system of record/database.

Machine downtime is captured.

Part serial numbers or batch numbers are available.

Defect data is captured.

Part serial numbers or batch numbers with timestamps are captured at each process step.

Cloud and Security Strategy

The system of record (e.g., historian) is securely accessible from outside of the company.

Other important data sources (ERP, MES, MRO, supplier data, etc.) are securely accessible from outside of the company.

The customer has the ability to work on the cloud, and has a clear policy for cloud providers.

Data segregation requirements are clearly defined OR the customer does not need data segregation.

A system is in place to segregate sensitive data from routine data OR the customer does not need data segregation.

Data Awareness

The customer has adopted a long-term data strategy.

Documentation or expertise exists to interpret the data coming from the machines and understand how that maps to the physical process.

Considering the responses to the assessment, you can determine how prepared the customer is technologically, and suggest appropriate next steps. For more information, see Managing Customer Expectations in Understanding the Digital Readiness Index.

Using DRI to Assess Product and Application Fit

The DRI assessment tool helps customers map their strengths and weaknesses, and thereby gain a better understanding of which projects deliver immediate value and where they should invest in building advanced capabilities. Through our work with Fortune Global 500 (or G500) manufacturers, we have determined the main success factors essential for digital transformation. We found that plants tend to cluster in five areas along the two readiness axes, which we refer to as Digital Readiness Index Zones.

Each DRI Zone contains several typical attributes that help identify in-scope projects and areas for future development.

Digital Readiness Zone Attributes


Typical Attributes

In-Scope Projects

Typical Development Areas

Connection Zone

· Machines are not capturing relevant data or are unconnected.

· Data is not flowing to system of record and/or no cloud strategy is in place.

· Build offline data acumen

· Develop foundational data visibility

· Advocacy for Digital Transformation

· Work with partners to connect

Visibility Zone

· Machines have sensors and are capturing some data.

· Limited expertise on data-process relationships exists.

· Limited/no change management capability exists.

· Global operations view

· Statistical process control

· Parameter relationships

· Building alignment between corporate, plant operations, and IT on a Digital Manufacturing strategy (data, technology, governance)

· Building expertise on data/process relationships

Efficiency Zone

· Good technical connectivity and data capability exist.

· Limited capability for insights and custom application development exists.

· Machine performance (OEE)

· Part traceability

· High-level defect analysis

· Accelerating Data Science capability

· Enhancing change management capability to enable real-time response

· Refine strategy for data connection

Advanced Analytics Zone

· Excellent technical connectivity and data capability exist.

· Some ability to develop custom applications exists.

· Limited/moderate change management capability exists.

· Advanced statistical techniques to identify root causes

· Predictive analysis

· Extensible analytics

· Platform third-party development

· Developing new metrics and incentives for operations

· Supplier alignment on digital strategy

Transformation Zone

· Comprehensive technical excellence across the extended enterprise exists.

· Executive leadership has a focus on transformation.

· Advanced change management capability exists.

· Cross-system analysis

· Supplier optimization

· Real-time capacity-based pricing

· New business model innovation

· Supply chain transformation

Where Does the Customer Fit In?

Understanding which DRI zone the customer fits in does not mean that you are limited to suggesting only in-scope projects. Rather, the Digital Readiness Index Zone attributes help you understand the prerequisites the customer will need to work on, which is critical for helping you establish realistic budget and timeline expectations. Based on this knowledge, you can help determine which of our Sight Machine products and/or applications make the most sense to recommend.

Managing Customer Expectations

What’s the difference between a successful digital manufacturing project and one that fails to meet its objectives? In many cases, manufacturers start off on projects that are not well-suited to the levels of technical or organizational readiness of their individual plants or the enterprise itself.

Through careful project planning and communication, you can help customers select the right projects, while at the same time manage their expectations.

Considering Common Data Issues

It is essential that you fully evaluate the customer’s data landscape rather than relying solely on their answers in the Technical Readiness assessment because their own viewpoint may not be entirely accurate. For more information , see Evaluating the Project Landscape.

If you do not properly evaluate the customer’s technical readiness, you may experience a number of challenges related to the data itself, including:

  • Data Issues: The data capabilities of the customer’s specific plants and/or lines may not be suitable for the projects selected. Consider the following factors:
    • Data Availability: Does the customer have the data payload available in real time?
    • Data Quality: Does the customer have data in which reading a clean signal is straightforward? For example, the customer’s quality records are inconsistently formatted or critical machines do not include the needed sensors (or are unconnected).
    • Data Fitness: Does the customer’s data map to the problem space/value proposition? For example, the customer lacks serialization in their process.
  • Knowledge and Operational Gaps: Plants need an onsite team that understands relationships between data and processes (i.e., how the data generated by machine sensors represents the manufacturing process). Manufacturers often lack the expertise or have not committed the right staff to the project. Operationally, it is critical that the local team is brought into the project and has the change management capability to impact the changes outlined in the project objectives.
  • Inability to Scale: The highest value is achieved when digital manufacturing projects scale across multiple machines, lines, and facilities, making use of a broader set of data and applying the resulting insights wherever they are relevant. To accomplish this, a project must be designed to scale both to adjacent processes and to like processes. The following factors may prohibit scaling:
    • Manual manipulation of data
    • Wide variation in data sources (e.g., not installing software updates so machines are running different versions of software)
    • Lack of IT resources

Avoiding a Narrow Project Scope

Whenever possible, steer the customer toward a wider project scope. Promising to resolve a single issue or provide a solution for a unique error that is not repeatable will not have as much impact on the digital transformation of the enterprise as a whole. Instead, work with the customer to find a solution that, even at limited scope, can be used to understand and solve many types of issues that will improve the entire enterprise. Through the data analysis, our manufacturing analytics platform can be used to find that interesting and much-needed improvement.

Consider the following issues that arise if the project scope is too narrow:

  • Testing the Technology (i.e., Explaining Something Known): The customer finds an issue and works internally to resolve it (sometimes using manual, one-off fixes). The customer now wants our Sight Machine Data Science team to analyze the data to come up with the exact same solution to prove that the platform works. The problem is that the customer’s original solution was a patchwork, based on too many factors that we may not be able to replicate.
  • Validating Against Hunches (i.e., Explaining Someone’s Guess): This is similar to the hypothetical situation above, but this time the customer has not clearly identified and resolved the problem. Instead, the customer has a hunch and wants our Sight Machine Data Science team to analyze data to confirm that bias. In this situation, the data may not actually confirm the hunch.